Problem, research strategy, and findings: Few studies have examined ride-hail users’ individual preferences between ride-hail and transit. Based on a survey of ride-hail users in the Philadelphia (PA) region, I examine who uses ride-hail and investigate ride-hail users’ willingness to use ride-hail versus transit. My results suggest that more than one-quarter of respondents replaced transit with ride-hail in their last ride-hail trips. Mixed logit regression analysis based on stated preference choice experiments indicate that higher-income respondents and respondents over 30 years old are increasingly willing to choose ride-hail over transit, even though their actual ride-hail usage is lower than that among lower-income and younger respondents. Results also show that female respondents are more willing to choose ride-hail over transit than male respondents and less frequent transit users are more likely to choose ride-hail than frequent transit users. Higher cost and longer trip duration are significant deterrents for travel by either mode. Respondents consider the time spent on walking to and from transit more burdensome than in-vehicle travel time and wait time for transit and ride-hail. They consider waiting for ride-hail less burdensome than waiting for transit. Survey sampling and design limitations provide lessons for future ride-hail studies. Takeaway for practice: Practitioners should ensure convenient, affordable travel options for lower-income residents, who are more frequent but less willing ride-hail users than higher-income residents. Female respondents’ safety concerns about transit should urge transit agencies to recognize female transit riders’ travel needs. The relationship between age and willingness to use ride-hail reminds planners to anticipate greater substitution of ride-hail for transit as the more tech-savvy generation starts entering their 30s. Last, fare reduction alone may not be enough to prompt ride-hail users to switch to transit. Service improvements that shorten the overall trip duration are imperative to make transit more attractive.

This study uses multilevel negative binomial models to investigate relationships between neighborhood socio-demographics, urban form, roadway characteristics, traffic collisions, injuries, and fatalities on the Philadelphia region’s streets from 2010 to 2014. We pay particular attention to neighborhood population density. Results indicate that streets in denser neighborhoods have fewer overall collisions, injuries, and fatalities. The association with pedestrian safety is mixed and somewhat uncertain across urban areas and model specifications. This study highlights the importance of population density in traffic safety and helps explain some of the variation in findings across studies examining the relationship between urban form and pedestrian safety.

This study investigates the relationship between pedestrian crashes and various socio-demographic, built environment, traffic exposure, and roadway characteristics across different times of day for both weekdays and weekends. Using the street segment as the unit of analysis, multilevel generalized linear mixed models with negative binomial estimators are applied to examine predictors of pedestrian crashes, including those resulting in severe injuries and fatalities, that occurred in Philadelphia, U.S., between 2010 and 2017. It is found that most of the relationships between the predictor variables and pedestrian crashes are consistent throughout the day for both weekdays and weekends. Although traffic volumes and pedestrian trips fluctuate throughout the day, average daily measures of traffic and pedestrian exposure have consistent relationships with pedestrian crashes throughout the day for both weekdays and weekends. Certain roadway characteristics, such as the amount of secondary highways and major arterials, have stronger relationships with pedestrian crashes than others at certain times of day. Results indicate that authorities should pay particular attention to pedestrian safety at night, as well as in lower-income neighborhoods throughout the day when designing interventions to improve the walking environment. Modeling pedestrian crashes by time of day provides additional information that might not be captured by temporally aggregate analyses. Scholars should consider incorporating time of day into future traffic crash analyses.